Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

NMR Spectroscopy in the Conformational Analysis of Peptides: An Overview

BACKGROUND

NMR spectroscopy is one of the most powerful tools to study the structure and interaction properties of peptides and proteins from a dynamic perspective. Knowing the bioactive conformations of peptides is crucial in the drug discovery field to design more efficient analogue ligands and inhibitors of protein-protein interactions targeting therapeutically relevant systems.

OBJECTIVE

This review provides a toolkit to investigate peptide conformational properties by NMR.

METHODS

Articles cited herein, related to NMR studies of peptides and proteins were mainly searched through PubMed and the web. More recent and old books on NMR spectroscopy written by eminent scientists in the field were consulted as well.

RESULTS

The review is mainly focused on NMR tools to gain the 3D structure of small unlabeled peptides. It is more application-oriented as it is beyond its goal to deliver a profound theoretical background. However, the basic principles of 2D homonuclear and heteronuclear experiments are briefly described. Protocols to obtain isotopically labeled peptides and principal triple resonance experiments needed to study them, are discussed as well.

CONCLUSION

NMR is a leading technique in the study of conformational preferences of small flexible peptides whose structure can be often only described by an ensemble of conformations. Although NMR studies of peptides can be easily and fast performed by canonical protocols established a few decades ago, more recently we have assisted to tremendous improvements of NMR spectroscopy to investigate instead large systems and overcome its molecular weight limit.

High-throughput mutagenesis reveals unique structural features of human ADAR1

Adenosine Deaminases that act on RNA (ADARs) are enzymes that catalyze adenosine to inosine conversion in dsRNA, a common form of RNA editing. Mutations in the human ADAR1 gene are known to cause disease and recent studies have identified ADAR1 as a potential therapeutic target for a subset of cancers. However, efforts to define the mechanistic effects for disease associated ADAR1 mutations and the rational design of ADAR1 inhibitors are limited by a lack of structural information. Here, we describe the combination of high throughput mutagenesis screening studies, biochemical characterization and Rosetta-based structure modeling to identify unique features of ADAR1. Importantly, these studies reveal a previously unknown zinc-binding site on the surface of the ADAR1 deaminase domain which is important for ADAR1 editing activity. Furthermore, we present structural models that explain known properties of this enzyme and make predictions about the role of specific residues in a surface loop unique to ADAR1.

CS-ROSETTA

Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.

POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins

Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25-78%, with the largest improvements observed for ¹Hα and ¹³C'. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org .

Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra

Automated methods for NMR structure determination of proteins are continuously becoming more robust. However, current methods addressing larger, more complex targets rely on analyzing 6-10 complementary spectra, suggesting the need for alternative approaches. Here, we describe 4D-CHAINS/autoNOE-Rosetta, a complete pipeline for NOE-driven structure determination of medium- to larger-sized proteins. The 4D-CHAINS algorithm analyzes two 4D spectra recorded using a single, fully protonated protein sample in an iterative ansatz where common NOEs between different spin systems supplement conventional through-bond connectivities to establish assignments of sidechain and backbone resonances at high levels of completeness and with a minimum error rate. The 4D-CHAINS assignments are then used to guide automated assignment of long-range NOEs and structure refinement in autoNOE-Rosetta. Our results on four targets ranging in size from 15.5 to 27.3 kDa illustrate that the structures of proteins can be determined accurately and in an unsupervised manner in a matter of days.

Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pK_a determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.

Modern Technologies of Solution Nuclear Magnetic Resonance Spectroscopy for Three-dimensional Structure Determination of Proteins Open Avenues for Life Scientists

Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for structural studies of chemical compounds and biomolecules such as DNA and proteins. Since the NMR signal sensitively reflects the chemical environment and the dynamics of a nuclear spin, NMR experiments provide a wealth of structural and dynamic information about the molecule of interest at atomic resolution. In general, structural biology studies using NMR spectroscopy still require a reasonable understanding of the theory behind the technique and experience on how to recorded NMR data. Owing to the remarkable progress in the past decade, we can easily access suitable and popular analytical resources for NMR structure determination of proteins with high accuracy. Here, we describe the practical aspects, workflow and key points of modern NMR techniques used for solution structure determination of proteins. This review should aid NMR specialists aiming to develop new methods that accelerate the structure determination process, and open avenues for non-specialist and life scientists interested in using NMR spectroscopy to solve protein structures.

NMR-based automated protein structure determination

NMR spectra analysis for protein structure determination can now in many cases be performed by automated computational methods. This overview of the computational methods for NMR protein structure analysis presents recent automated methods for signal identification in multidimensional NMR spectra, sequence-specific resonance assignment, collection of conformational restraints, and structure calculation, as implemented in the CYANA software package. These algorithms are sufficiently reliable and integrated into one software package to enable the fully automated structure determination of proteins starting from NMR spectra without manual interventions or corrections at intermediate steps, with an accuracy of 1-2 Å backbone RMSD in comparison with manually solved reference structures.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

A Framework to Simplify Combined Sampling Strategies in Rosetta

A core task in computational structural biology is the search of conformational space for low energy configurations of a biological macromolecule. Because conformational space has a very high dimensionality, the most successful search methods integrate some form of prior knowledge into a general sampling algorithm to reduce the effective dimensionality. However, integrating multiple types of constraints can be challenging. To streamline the incorporation of diverse constraints, we developed the Broker: an extension of the Rosetta macromolecular modeling suite that can express a wide range of protocols using constraints by combining small, independent modules, each of which implements a different set of constraints. We demonstrate expressiveness of the Broker through several code vignettes. The framework enables rapid protocol development in both biomolecular design and structural modeling tasks and thus is an important step towards exposing the rich functionality of Rosetta's core libraries to a growing community of users addressing a diverse set of tasks in computational biology.

NMR data-driven structure determination using NMR-I-TASSER in the CASD-NMR experiment

NMR-I-TASSER, an adaption of the I-TASSER algorithm combining NMR data for protein structure determination, recently joined the second round of the CASD-NMR experiment. Unlike many molecular dynamics-based methods, NMR-I-TASSER takes a molecular replacement-like approach to the problem by first threading the target through the PDB to identify structural templates which are then used for iterative NOE assignments and fragment structure assembly refinements. The employment of multiple templates allows NMR-I-TASSER to sample different topologies while convergence to a single structure is not required. Retroactive and blind tests of the CASD-NMR targets from Rounds 1 and 2 demonstrate that even without using NOE peak lists I-TASSER can generate correct structure topology with 15 of 20 targets having a TM-score above 0.5. With the addition of NOE-based distance restraints, NMR-I-TASSER significantly improved the I-TASSER models with all models having the TM-score above 0.5. The average RMSD was reduced from 5.29 to 2.14 Å in Round 1 and 3.18 to 1.71 Å in Round 2. There is no obvious difference in the modeling results with using raw and refined peak lists, indicating robustness of the pipeline to the NOE assignment errors. Overall, despite the low-resolution modeling the current NMR-I-TASSER pipeline provides a coarse-grained structure folding approach complementary to traditional molecular dynamics simulations, which can produce fast near-native frameworks for atomic-level structural refinement.

The second round of Critical Assessment of Automated Structure Determination of Proteins by NMR: CASD-NMR-2013

The second round of the community-wide initiative Critical Assessment of automated Structure Determination of Proteins by NMR (CASD-NMR-2013) comprised ten blind target datasets, consisting of unprocessed spectral data, assigned chemical shift lists and unassigned NOESY peak and RDC lists, that were made available in both curated (i.e. manually refined) or un-curated (i.e. automatically generated) form. Ten structure calculation programs, using fully automated protocols only, generated a total of 164 three-dimensional structures (entries) for the ten targets, sometimes using both curated and un-curated lists to generate multiple entries for a single target. The accuracy of the entries could be established by comparing them to the corresponding manually solved structure of each target, which was not available at the time the data were provided. Across the entire data set, 71 % of all entries submitted achieved an accuracy relative to the reference NMR structure better than 1.5 Å. Methods based on NOESY peak lists achieved even better results with up to 100% of the entries within the 1.5 Å threshold for some programs. However, some methods did not converge for some targets using un-curated NOESY peak lists. Over 90% of the entries achieved an accuracy better than the more relaxed threshold of 2.5 Å that was used in the previous CASD-NMR-2010 round. Comparisons between entries generated with un-curated versus curated peaks show only marginal improvements for the latter in those cases where both calculations converged.

Guiding automated NMR structure determination using a global optimization metric, the NMR DP score

ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.

Analysis of the performance of the CHESHIRE and YAPP methods at CASD-NMR round 3

We present an analysis of the results obtained at CASD-NMR round 3 by the CHESHIRE and the YAPP methods. To determine protein structures, the CHESHIRE method uses solely information provided by NMR chemical shifts, while the YAPP method uses an automated assignment of NOESY spectra. Of the ten targets of CASD-NMR round 3, nine CHESHIRE predictions and eight YAPP ones were submitted. The eight YAPP predictions ranged from 0.7 to 1.9 Å Cα accuracy, with an average of 1.3 Å. The nine CHESHIRE predictions ranged from 0.8 to 2.6 Å Cα accuracy for the ordered regions of the proteins, with an average of 1.6 Å. Taken together, these results illustrate how the NOESY based YAPP method and the chemical shift based CHESHIRE method can provide structures of comparable quality.

Combined automated NOE assignment and structure calculation with CYANA

The automated assignment of NOESY cross peaks has become a fundamental technique for NMR protein structure analysis. A widely used algorithm for this purpose is implemented in the program CYANA. It has been used for a large number of structure determinations of proteins in solution but was so far not described in full detail. In this paper we present a complete description of the CYANA implementation of automated NOESY assignment, which differs extensively from its predecessor CANDID by the use of a consistent probabilistic treatment, and we discuss its performance in the second round of the critical assessment of structure determination by NMR.

Improved reliability, accuracy and quality in automated NMR structure calculation with ARIA

In biological NMR, assignment of NOE cross-peaks and calculation of atomic conformations are critical steps in the determination of reliable high-resolution structures. ARIA is an automated approach that performs NOE assignment and structure calculation in a concomitant manner in an iterative procedure. The log-harmonic shape for distance restraint potential and the Bayesian weighting of distance restraints, recently introduced in ARIA, were shown to significantly improve the quality and the accuracy of determined structures. In this paper, we propose two modifications of the ARIA protocol: (1) the softening of the force field together with adapted hydrogen radii, which is meaningful in the context of the log-harmonic potential with Bayesian weighting, (2) a procedure that automatically adjusts the violation tolerance used in the selection of active restraints, based on the fitting of the structure to the input data sets. The new ARIA protocols were fine-tuned on a set of eight protein targets from the CASD-NMR initiative. As a result, the convergence problems previously observed for some targets was resolved and the obtained structures exhibited better quality. In addition, the new ARIA protocols were applied for the structure calculation of ten new CASD-NMR targets in a blind fashion, i.e. without knowing the actual solution. Even though optimisation of parameters and pre-filtering of unrefined NOE peak lists were necessary for half of the targets, ARIA consistently and reliably determined very precise and highly accurate structures for all cases. In the context of integrative structural biology, an increasing number of experimental methods are used that produce distance data for the determination of 3D structures of macromolecules, stressing the importance of methods that successfully make use of ambiguous and noisy distance data.

CASD-NMR 2: robust and accurate unsupervised analysis of raw NOESY spectra and protein structure determination with UNIO

UNIO is a comprehensive software suite for protein NMR structure determination that enables full automation of all NMR data analysis steps involved--including signal identification in NMR spectra, sequence-specific backbone and side-chain resonance assignment, NOE assignment and structure calculation. Within the framework of the second round of the community-wide stringent blind NMR structure determination challenge (CASD-NMR 2), we participated in two categories of CASD-NMR 2, namely using either raw NMR spectra or unrefined NOE peak lists as input. A total of 15 resulting NMR structure bundles were submitted for 9 out of 10 blind protein targets. All submitted UNIO structures accurately coincided with the corresponding blind targets as documented by an average backbone root mean-square deviation to the reference proteins of only 1.2 Å. Also, the precision of the UNIO structure bundles was virtually identical to the ensemble of reference structures. By assessing the quality of all UNIO structures submitted to the two categories, we find throughout that only the UNIO-ATNOS/CANDID approach using raw NMR spectra consistently yielded structure bundles of high quality for direct deposition in the Protein Data Bank. In conclusion, the results obtained in CASD-NMR 2 are another vital proof for robust, accurate and unsupervised NMR data analysis by UNIO for real-world applications.

Systematic evaluation of combined automated NOE assignment and structure calculation with CYANA

The automated assignment of NOESY cross peaks has become a fundamental technique for NMR protein structure analysis. A widely used algorithm for this purpose is implemented in the program CYANA. It has been used for a large number of structure determinations of proteins in solution but a systematic evaluation of its performance has not yet been reported. In this paper we systematically analyze the reliability of combined automated NOESY assignment and structure calculation with CYANA under a variety of conditions on the basis of the experimental NMR data sets of ten proteins. To evaluate the robustness of the algorithm, the original high-quality experimental data sets were modified in different ways to simulate the effect of data imperfections, i.e. incomplete or erroneous chemical shift assignments, missing NOESY cross peaks, inaccurate peak positions, inaccurate peak intensities, lower dimensionality NOESY spectra, and higher tolerances for the matching of chemical shifts and peak positions. The results show that the algorithm is remarkably robust with regard to imperfections of the NOESY peak lists and the chemical shift tolerances but susceptible to lacking or erroneous resonance assignments, in particular for nuclei that are involved in many NOESY cross peaks.

High-resolution structure of the Shigella type-III secretion needle by solid-state NMR and cryo-electron microscopy

We introduce a general hybrid approach for determining the structures of supramolecular assemblies. Cryo-electron microscopy (cryo-EM) data define the overall envelope of the assembly and rigid-body orientation of the subunits while solid-state NMR (ssNMR) chemical shifts and distance constraints define the local secondary structure, protein fold and inter-subunit interactions. Finally, Rosetta structure calculations provide a general framework to integrate the different sources of structural information. Combining a 7.7-Å cryo-EM density map and 996 ssNMR distance constraints, the structure of the Type-III Secretion System (T3SS) needle of Shigella flexneri is determined to a precision of 0.4 Å. The calculated structures are cross-validated using an independent dataset of 691 ssNMR constraints and STEM measurements. The hybrid model resolves the conformation of the non-conserved N-terminus, that occupies a protrusion in the cryo-EM density, and reveals conserved pore residues forming a continuous pattern of electrostatic interactions, thereby suggesting a mechanism for effector protein translocation.

Automatic NOESY assignment in CS-RASREC-Rosetta

We have developed an approach for simultaneous structure calculation and automatic Nuclear Overhauser Effect (NOE) assignment to solve nuclear magnetic resonance (NMR) structures from unassigned NOESY data. The approach, autoNOE-Rosetta, integrates Resolution Adapted Structural RECombination (RASREC) Rosetta NMR calculations with algorithms for automatic NOE assignment. The method was applied to two proteins in the 15-20 kDa size range for which both, NMR and X-ray data, is available. The autoNOE-Rosetta calculations converge for both proteins and yield accurate structures with an RMSD of 1.9 Å to the X-ray reference structures. The method greatly expands the radius of convergence for automatic NOE assignment, and should be broadly useful for NMR structure determination.